An Intelligent System for the Enhancement of Visually Impaired Navigation and Disaster Assistance using Geo-Based Positioning and Machine Learning

Technological advancement has brought the masses unprecedented convenience, but unnoticed by many, a population neglected through the age of technology has been the visually impaired population. The visually impaired population has grown through ages with as much desire as everyone else to adventure but lack the confidence and support to do so. Time has transported society to a new phase condensed in big data, but to the visually impaired population, this quick-pace living lifestyle, along with the unpredictable nature of natural disaster and COVID-19 pandemic, has dropped them deeper into a feeling of disconnection from the society. Our application uses the global positioning system to support the visually impaired in independent navigation, alerts them in face of natural disasters, and reminds them to sanitize their devices during the COVID-19 pandemic.

Face Expression Recognition using Convolution Neural Network (CNN) Models

This paper proposes the design of a Facial Expression Recognition (FER) system based on deep convolutional neural network by using three model. In this work, a simple solution for facial expression recognition that uses a combination of algorithms for face detection, feature extraction and classification is discussed. The proposed method uses CNN models with SVM classifier and evaluates them, these models are Alex-net model, VGG-16 model and Res-Net model. Experiments are carried out on the Extended Cohn-Kanada (CK+) datasets to determine the recognition accuracy for the proposed FER system. In this study the accuracy of AlexNet model compared with Vgg16 model and ResNet model. The result show that AlexNet model achieved the best accuracy (88.2%) compared to other models.